This lecture shows how to use genetic algorithms to tune the parameters of a PID controller. Tuning a PID controller with genetic algorithms is not generally recommended, but is used to demonstrate evolutionary control algorithms.
Machine LearningControl
T. Duriez, S. L. Brunton, and B. R. Noack
https://www.springer.com/us/book/9783319406237
Closed-Loop Turbulence Control: Progress and Challenges
S. L. Brunton and B. R. Noack
http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleid=2423069
Code: faculty.washington.edu/sbrunton/DataDrivenControl.zip
https://www.eigensteve.com/

published:11 Jun 2018

views:2330

This lecture shows how to use genetic algorithms to tune the parameters of a PID controller. Tuning a PID controller with genetic algorithms is not generally recommended, but is used to demonstrate evolutionary control algorithms.
Machine LearningControl
T. Duriez, S. L. Brunton, and B. R. Noack
https://www.springer.com/us/book/9783319406237
Closed-Loop Turbulence Control: Progress and Challenges
S. L. Brunton and B. R. Noack
http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleid=2423069
Code: faculty.washington.edu/sbrunton/DataDrivenControl.zip
https://www.eigensteve.com/

published:11 Jun 2018

views:826

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of propagation models.
The idea is measuring the level of electromagnetic fields with a test car (which we have) around the test transmitters. Then, the results are used to develop the parameters of propagation model. The parameters depend on the type of terrain. That is why we plan to measure the many locations across Europe.
We believe that this will contribute greatly to the development of radio communication technology.
More info: j.jarzina@tele-com.poznan.pl
+48 602 42 66 97

published:01 Jul 2015

views:654

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workbench®, one of four components of FICO® XpressOptimization that you learned about in the first tutorial in the series. The internal algorithms of Xpress Optimizer are user customizable through control parameters. With Xpress Tuner, you can find a favorable set of control parameters that allow the Xpress Optimizer to solve a MIP problem (or set of problems) faster than using the default settings. It is designed to be easy to use so you do not have to be an expert to use Xpress Tuner. This is the final video of the 18-part tutorial series on Xpress Mosel, FICO’s market-leading analytic orchestration, optimization modeling, and programming language. To learn more, please visit www.fico.com/optimization.

published:14 Jun 2018

views:106

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the machine learning algorithm, please give it a thumbs up and we can produce more of such videos to help you get the most value from your UBA App.

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on your P, PI or PID controller. Thankfully these methods are extremely simple to understand and implement.

published:11 Jul 2015

views:129208

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any contribution for my work on Patreon - https://www.patreon.com/briandouglas
In this video, I introduce the topic of PID control. This is a short introduction design to prepare you for the next few lectures where I will go through several examples of PID control. This video explains why we need feedback control and how PID controller are simple and efficient ways to ensure you have a feed back system that meets your requirements.
I will be loading a new video each week and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!
Don't forget to subscribe! Follow me on Twitter @BrianBDouglas!

published:14 Dec 2012

views:730117

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird
This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm.
The program is written in HTML5 using Phaser framework (http://phaser.io/). There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) to implement entire artificial neural network instead of making a new one from the scratch.
Download source code here:
https://github.com/ssusnic/Machine-Learning-Flappy-Bird
-----------------------------------------------------------------
According to Arthur Samuel, machine learning is the science of getting computers to act without being explicitly programmed. It is a fine tuning process of learning that incrementally improves an initial random system.
The form of machine learning implemented in this program uses a genetic algorithm to train artificial neural networks.
The birds are learning how to flap optimally in order to fly safely through barriers as long as possible.
The main concept is based on these 3 steps:
1. creating the initial population of 10 birds randomly
2. learning as the game is being played
3. applying natural evolution to form the next improved population
-----------------------------------------------------------------
To play the game, each bird has its own neural network consisted of the next 3 layers:
1. an input layer with 2 neurons representing what a bird sees:
- horizontal distance to the closest gap
- height difference to the closest gap
2. a hidden layer with 6 neurons
3. an output layer with 1 neuron to perform flap if its value is greater than 0.5
-----------------------------------------------------------------
To measure the quality of birds and select the best ones, for each bird is calculated its fitness function in this way:
- reward a bird by its total traveled distance
- penalize a bird by its current distance to the closest gap
When the entire population is dead, the fittest four birds are selected to breed a new population by using genetic algorithm operators: selection, crossover and mutation.
-----------------------------------------------------------------
Visit us:
http://www.askforgametask.com
Like us:
https://www.facebook.com/askforgametask
Follow us:
https://twitter.com/ssusnic
Music:
"BedtimeTune" by Jay Man
http://www.ourmusicbox.com

published:10 Aug 2017

views:579858

For more information, see http://nu32.org. This video is a supplement to the book "Embedded Computing and Mechatronics with the PIC32 Microcontroller," Lynch, Marchuk, and Elwin. It is part of Northwestern University's ME 333Introduction to Mechatronics.
L-comp: Limiting the maximum value of the integral term eint is called "integrator antiwindup." (You are preventing the integrator from "winding up" to a large value.) In terms of the maximum control effort u available from the actuator and the integral gain Ki, what might be a reasonable choice for the maximum value of eint? (Think about it; there is no single right answer, and in fact integrator antiwindup can be implemented in a number of different ways.)

Tuning practice

Tuning is the process of adjusting the pitch of one or many tones from musical instruments to establish typical intervals between these tones. Tuning is usually based on a fixed reference, such as A = 440Hz. Out of tune refers to a pitch/tone that is either too high (sharp) or too low (flat) in relation to a given reference pitch. While an instrument might be in tune relative to its own range of notes, it may not be considered 'in tune' if it does not match A = 440Hz (or whatever reference pitch one might be using). Some instruments become 'out of tune' with damage or time and must be readjusted or repaired.

Different methods of sound production require different methods of adjustment:

Tuning to a pitch with one's voice is called matching pitch and is the most basic skill learned in ear training.

Machine Learning (journal)

In 2001, forty editors and members of the editorial board of Machine Learning resigned in order to support the Journal of Machine Learning Research (JMLR), saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. Instead, they wrote, they supported the model of JMLR, in which authors retained copyright over their papers and archives were freely available on the internet.

Methodology

Optimization problems

In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible.

The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population, and each individual's genome is modified (recombined and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

This lecture shows how to use genetic algorithms to tune the parameters of a PID controller. Tuning a PID controller with genetic algorithms is not generally recommended, but is used to demonstrate evolutionary control algorithms.
Machine LearningControl
T. Duriez, S. L. Brunton, and B. R. Noack
https://www.springer.com/us/book/9783319406237
Closed-Loop Turbulence Control: Progress and Challenges
S. L. Brunton and B. R. Noack
http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleid=2423069
Code: faculty.washington.edu/sbrunton/DataDrivenControl.zip
https://www.eigensteve.com/

1:04

Calibration or tuning of radio propagation models (algorithms)

Calibration or tuning of radio propagation models (algorithms)

Calibration or tuning of radio propagation models (algorithms)

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of propagation models.
The idea is measuring the level of electromagnetic fields with a test car (which we have) around the test transmitters. Then, the results are used to develop the parameters of propagation model. The parameters depend on the type of terrain. That is why we plan to measure the many locations across Europe.
We believe that this will contribute greatly to the development of radio communication technology.
More info: j.jarzina@tele-com.poznan.pl
+48 602 42 66 97

4:07

FICO® Xpress Mosel #18: Tuning Xpress Solver and Algorithms

FICO® Xpress Mosel #18: Tuning Xpress Solver and Algorithms

FICO® Xpress Mosel #18: Tuning Xpress Solver and Algorithms

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workbench®, one of four components of FICO® XpressOptimization that you learned about in the first tutorial in the series. The internal algorithms of Xpress Optimizer are user customizable through control parameters. With Xpress Tuner, you can find a favorable set of control parameters that allow the Xpress Optimizer to solve a MIP problem (or set of problems) faster than using the default settings. It is designed to be easy to use so you do not have to be an expert to use Xpress Tuner. This is the final video of the 18-part tutorial series on Xpress Mosel, FICO’s market-leading analytic orchestration, optimization modeling, and programming language. To learn more, please visit www.fico.com/optimization.

5:54

IBM QRadar UBA Tuning Machine Learning Algorithms

IBM QRadar UBA Tuning Machine Learning Algorithms

IBM QRadar UBA Tuning Machine Learning Algorithms

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the machine learning algorithm, please give it a thumbs up and we can produce more of such videos to help you get the most value from your UBA App.

PID Tuning: The Ziegler Nichols Method Explained

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on your P, PI or PID controller. Thankfully these methods are extremely simple to understand and implement.

7:44

PID Control - A brief introduction

PID Control - A brief introduction

PID Control - A brief introduction

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any contribution for my work on Patreon - https://www.patreon.com/briandouglas
In this video, I introduce the topic of PID control. This is a short introduction design to prepare you for the next few lectures where I will go through several examples of PID control. This video explains why we need feedback control and how PID controller are simple and efficient ways to ensure you have a feed back system that meets your requirements.
I will be loading a new video each week and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!
Don't forget to subscribe! Follow me on Twitter @BrianBDouglas!

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird
This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm.
The program is written in HTML5 using Phaser framework (http://phaser.io/). There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) to implement entire artificial neural network instead of making a new one from the scratch.
Download source code here:
https://github.com/ssusnic/Machine-Learning-Flappy-Bird
-----------------------------------------------------------------
According to Arthur Samuel, machine learning is the science of getting computers to act without being explicitly programmed. It is a fine tuning process of learning that incrementally improves an initial random system.
The form of machine learning implemented in this program uses a genetic algorithm to train artificial neural networks.
The birds are learning how to flap optimally in order to fly safely through barriers as long as possible.
The main concept is based on these 3 steps:
1. creating the initial population of 10 birds randomly
2. learning as the game is being played
3. applying natural evolution to form the next improved population
-----------------------------------------------------------------
To play the game, each bird has its own neural network consisted of the next 3 layers:
1. an input layer with 2 neurons representing what a bird sees:
- horizontal distance to the closest gap
- height difference to the closest gap
2. a hidden layer with 6 neurons
3. an output layer with 1 neuron to perform flap if its value is greater than 0.5
-----------------------------------------------------------------
To measure the quality of birds and select the best ones, for each bird is calculated its fitness function in this way:
- reward a bird by its total traveled distance
- penalize a bird by its current distance to the closest gap
When the entire population is dead, the fittest four birds are selected to breed a new population by using genetic algorithm operators: selection, crossover and mutation.
-----------------------------------------------------------------
Visit us:
http://www.askforgametask.com
Like us:
https://www.facebook.com/askforgametask
Follow us:
https://twitter.com/ssusnic
Music:
"BedtimeTune" by Jay Man
http://www.ourmusicbox.com

2:33

Improving the basic PID control algorithm (Kevin Lynch)

Improving the basic PID control algorithm (Kevin Lynch)

Improving the basic PID control algorithm (Kevin Lynch)

For more information, see http://nu32.org. This video is a supplement to the book "Embedded Computing and Mechatronics with the PIC32 Microcontroller," Lynch, Marchuk, and Elwin. It is part of Northwestern University's ME 333Introduction to Mechatronics.
L-comp: Limiting the maximum value of the integral term eint is called "integrator antiwindup." (You are preventing the integrator from "winding up" to a large value.) In terms of the maximum control effort u available from the actuator and the integral gain Ki, what might be a reasonable choice for the maximum value of eint? (Think about it; there is no single right answer, and in fact integrator antiwindup can be implemented in a number of different ways.)

13:59

Machine Learning Control: Genetic Algorithms

Machine Learning Control: Genetic Algorithms

Machine Learning Control: Genetic Algorithms

This lecture provides an overview of genetic algorithms, which can be used to tune the parameters of a control law.
Machine LearningControl
T. Duriez, S. L. Brunton, and B. R. Noack
https://www.springer.com/us/book/9783319406237
Closed-Loop Turbulence Control: Progress and Challenges
S. L. Brunton and B. R. Noack
http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleid=2423069
https://www.eigensteve.com/

This lecture shows how to use genetic algorithms to tune the parameters of a PID controller. Tuning a PID controller with genetic algorithms is not generally recommended, but is used to demonstrate evolutionary control algorithms.
Machine LearningControl
T. Duriez, S. L. Brunton, and B. R. Noack
https://www.springer.com/us/book/9783319406237
Closed-Loop Turbulence Control: Progress and Challenges
S. L. Brunton and B. R. Noack
http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleid=2423069
Code: faculty.washington.edu/sbrunton/DataDrivenControl.zip
https://www.eigensteve.com/

published: 11 Jun 2018

Calibration or tuning of radio propagation models (algorithms)

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of propagation models.
The idea is measuring the level of electromagnetic fields with a test car (which we have) around the test transmitters. Then, the results are used to develop the parameters of propagation model. The parameters depend on the type of terrain. That is why we plan to measure the many locations across Europe.
We believe that this will contribute greatly to the development of radio communication technology.
More info: j.jarzina@tele-com.poznan.pl
+48 602 42 66 97

published: 01 Jul 2015

FICO® Xpress Mosel #18: Tuning Xpress Solver and Algorithms

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workbench®, one of four components of FICO® XpressOptimization that you learned about in the first tutorial in the series. The internal algorithms of Xpress Optimizer are user customizable through control parameters. With Xpress Tuner, you can find a favorable set of control parameters that allow the Xpress Optimizer to solve a MIP problem (or set of problems) faster than using the default settings. It is designed to be easy to use so you do not have to be an expert to use Xpress Tuner. This is the final video of the 18-part tutorial series on Xpress Mosel, FICO’s market-leading analytic orchestration, optimization modeling, and program...

published: 14 Jun 2018

IBM QRadar UBA Tuning Machine Learning Algorithms

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the machine learning algorithm, please give it a thumbs up and we can produce more of such videos to help you get the most value from your UBA App.

SteerFit: Automated Parameter Tuning for Steering Algorithms

Hyperparameter Optimization - The Math of Intelligence #7

Hyperparameters are the magic numbers of machine learning. We're going to learn how to find them in a more intelligent way than just trial-and-error. We'll go over grid search, random search, and Bayesian Optimization. I'll also cover the difference between Bayesian and Frequentist probability.
Code for this video: https://github.com/llSourcell/hyperparameter_optimization_strategies
Noah's Winning Code:
https://github.com/NoahLidell/math-of-intelligence/tree/master/probability_theory
Hammad's Runner-up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Naive%20Bayes%20Classifier
More learning resources:
https://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf
https://thuijskens.github.io/2016...

PID Tuning: The Ziegler Nichols Method Explained

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on your P, PI or PID controller. Thankfully these methods are extremely simple to understand and implement.

published: 11 Jul 2015

PID Control - A brief introduction

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any contribution for my work on Patreon - https://www.patreon.com/briandouglas
In this video, I introduce the topic of PID control. This is a short introduction design to prepare you for the next few lectures where I will go through several examples of PID control. This video explains why we need feedback control and how PID controller are simple and efficient ways to ensure you have a feed back system that meets your requirements.
I will be loading a new video each week and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!
Don't forget to subscribe...

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird
This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm.
The program is written in HTML5 using Phaser framework (http://phaser.io/). There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) to implement entire artificial neural network instead of making a new one from the scratch.
Download source code here:
https://github.com/ssusnic/Machine-Learning-Flappy-Bird
-----------------------------------------------------------------
According to Arthur Samuel, machine learning is the sc...

published: 10 Aug 2017

Improving the basic PID control algorithm (Kevin Lynch)

For more information, see http://nu32.org. This video is a supplement to the book "Embedded Computing and Mechatronics with the PIC32 Microcontroller," Lynch, Marchuk, and Elwin. It is part of Northwestern University's ME 333Introduction to Mechatronics.
L-comp: Limiting the maximum value of the integral term eint is called "integrator antiwindup." (You are preventing the integrator from "winding up" to a large value.) In terms of the maximum control effort u available from the actuator and the integral gain Ki, what might be a reasonable choice for the maximum value of eint? (Think about it; there is no single right answer, and in fact integrator antiwindup can be implemented in a number of different ways.)

published: 08 Dec 2015

Machine Learning Control: Genetic Algorithms

This lecture provides an overview of genetic algorithms, which can be used to tune the parameters of a control law.
Machine LearningControl
T. Duriez, S. L. Brunton, and B. R. Noack
https://www.springer.com/us/book/9783319406237
Closed-Loop Turbulence Control: Progress and Challenges
S. L. Brunton and B. R. Noack
http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleid=2423069
https://www.eigensteve.com/

published: 11 Jun 2018

Hyperparameter Tuning in Practice (C2W3L03)

published: 25 Aug 2017

How to find the best model parameters in scikit-learn

In this video, you'll learn how to efficiently search for the optimal tuning parameters (or "hyperparameters") for your machine learning model in order to maximize its performance. I'll start by demonstrating an exhaustive "grid search" process using scikit-learn's GridSearchCV class, and then I'll compare it with RandomizedSearchCV, which can often achieve similar results in far less time.
Download the notebook: https://github.com/justmarkham/scikit-learn-videos
Grid search user guide: http://scikit-learn.org/stable/modules/grid_search.html
GridSearchCV documentation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
RandomizedSearchCV documentation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
C...

Calibration or tuning of radio propagation models (algorithms)

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of pro...

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of propagation models.
The idea is measuring the level of electromagnetic fields with a test car (which we have) around the test transmitters. Then, the results are used to develop the parameters of propagation model. The parameters depend on the type of terrain. That is why we plan to measure the many locations across Europe.
We believe that this will contribute greatly to the development of radio communication technology.
More info: j.jarzina@tele-com.poznan.pl
+48 602 42 66 97

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of propagation models.
The idea is measuring the level of electromagnetic fields with a test car (which we have) around the test transmitters. Then, the results are used to develop the parameters of propagation model. The parameters depend on the type of terrain. That is why we plan to measure the many locations across Europe.
We believe that this will contribute greatly to the development of radio communication technology.
More info: j.jarzina@tele-com.poznan.pl
+48 602 42 66 97

FICO® Xpress Mosel #18: Tuning Xpress Solver and Algorithms

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workb...

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workbench®, one of four components of FICO® XpressOptimization that you learned about in the first tutorial in the series. The internal algorithms of Xpress Optimizer are user customizable through control parameters. With Xpress Tuner, you can find a favorable set of control parameters that allow the Xpress Optimizer to solve a MIP problem (or set of problems) faster than using the default settings. It is designed to be easy to use so you do not have to be an expert to use Xpress Tuner. This is the final video of the 18-part tutorial series on Xpress Mosel, FICO’s market-leading analytic orchestration, optimization modeling, and programming language. To learn more, please visit www.fico.com/optimization.

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workbench®, one of four components of FICO® XpressOptimization that you learned about in the first tutorial in the series. The internal algorithms of Xpress Optimizer are user customizable through control parameters. With Xpress Tuner, you can find a favorable set of control parameters that allow the Xpress Optimizer to solve a MIP problem (or set of problems) faster than using the default settings. It is designed to be easy to use so you do not have to be an expert to use Xpress Tuner. This is the final video of the 18-part tutorial series on Xpress Mosel, FICO’s market-leading analytic orchestration, optimization modeling, and programming language. To learn more, please visit www.fico.com/optimization.

IBM QRadar UBA Tuning Machine Learning Algorithms

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the ...

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the machine learning algorithm, please give it a thumbs up and we can produce more of such videos to help you get the most value from your UBA App.

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the machine learning algorithm, please give it a thumbs up and we can produce more of such videos to help you get the most value from your UBA App.

PID Tuning: The Ziegler Nichols Method Explained

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on...

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on your P, PI or PID controller. Thankfully these methods are extremely simple to understand and implement.

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on your P, PI or PID controller. Thankfully these methods are extremely simple to understand and implement.

PID Control - A brief introduction

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any con...

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any contribution for my work on Patreon - https://www.patreon.com/briandouglas
In this video, I introduce the topic of PID control. This is a short introduction design to prepare you for the next few lectures where I will go through several examples of PID control. This video explains why we need feedback control and how PID controller are simple and efficient ways to ensure you have a feed back system that meets your requirements.
I will be loading a new video each week and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!
Don't forget to subscribe! Follow me on Twitter @BrianBDouglas!

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any contribution for my work on Patreon - https://www.patreon.com/briandouglas
In this video, I introduce the topic of PID control. This is a short introduction design to prepare you for the next few lectures where I will go through several examples of PID control. This video explains why we need feedback control and how PID controller are simple and efficient ways to ensure you have a feed back system that meets your requirements.
I will be loading a new video each week and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!
Don't forget to subscribe! Follow me on Twitter @BrianBDouglas!

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machi...

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird
This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm.
The program is written in HTML5 using Phaser framework (http://phaser.io/). There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) to implement entire artificial neural network instead of making a new one from the scratch.
Download source code here:
https://github.com/ssusnic/Machine-Learning-Flappy-Bird
-----------------------------------------------------------------
According to Arthur Samuel, machine learning is the science of getting computers to act without being explicitly programmed. It is a fine tuning process of learning that incrementally improves an initial random system.
The form of machine learning implemented in this program uses a genetic algorithm to train artificial neural networks.
The birds are learning how to flap optimally in order to fly safely through barriers as long as possible.
The main concept is based on these 3 steps:
1. creating the initial population of 10 birds randomly
2. learning as the game is being played
3. applying natural evolution to form the next improved population
-----------------------------------------------------------------
To play the game, each bird has its own neural network consisted of the next 3 layers:
1. an input layer with 2 neurons representing what a bird sees:
- horizontal distance to the closest gap
- height difference to the closest gap
2. a hidden layer with 6 neurons
3. an output layer with 1 neuron to perform flap if its value is greater than 0.5
-----------------------------------------------------------------
To measure the quality of birds and select the best ones, for each bird is calculated its fitness function in this way:
- reward a bird by its total traveled distance
- penalize a bird by its current distance to the closest gap
When the entire population is dead, the fittest four birds are selected to breed a new population by using genetic algorithm operators: selection, crossover and mutation.
-----------------------------------------------------------------
Visit us:
http://www.askforgametask.com
Like us:
https://www.facebook.com/askforgametask
Follow us:
https://twitter.com/ssusnic
Music:
"BedtimeTune" by Jay Man
http://www.ourmusicbox.com

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird
This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm.
The program is written in HTML5 using Phaser framework (http://phaser.io/). There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) to implement entire artificial neural network instead of making a new one from the scratch.
Download source code here:
https://github.com/ssusnic/Machine-Learning-Flappy-Bird
-----------------------------------------------------------------
According to Arthur Samuel, machine learning is the science of getting computers to act without being explicitly programmed. It is a fine tuning process of learning that incrementally improves an initial random system.
The form of machine learning implemented in this program uses a genetic algorithm to train artificial neural networks.
The birds are learning how to flap optimally in order to fly safely through barriers as long as possible.
The main concept is based on these 3 steps:
1. creating the initial population of 10 birds randomly
2. learning as the game is being played
3. applying natural evolution to form the next improved population
-----------------------------------------------------------------
To play the game, each bird has its own neural network consisted of the next 3 layers:
1. an input layer with 2 neurons representing what a bird sees:
- horizontal distance to the closest gap
- height difference to the closest gap
2. a hidden layer with 6 neurons
3. an output layer with 1 neuron to perform flap if its value is greater than 0.5
-----------------------------------------------------------------
To measure the quality of birds and select the best ones, for each bird is calculated its fitness function in this way:
- reward a bird by its total traveled distance
- penalize a bird by its current distance to the closest gap
When the entire population is dead, the fittest four birds are selected to breed a new population by using genetic algorithm operators: selection, crossover and mutation.
-----------------------------------------------------------------
Visit us:
http://www.askforgametask.com
Like us:
https://www.facebook.com/askforgametask
Follow us:
https://twitter.com/ssusnic
Music:
"BedtimeTune" by Jay Man
http://www.ourmusicbox.com

For more information, see http://nu32.org. This video is a supplement to the book "Embedded Computing and Mechatronics with the PIC32 Microcontroller," Lynch, Marchuk, and Elwin. It is part of Northwestern University's ME 333Introduction to Mechatronics.
L-comp: Limiting the maximum value of the integral term eint is called "integrator antiwindup." (You are preventing the integrator from "winding up" to a large value.) In terms of the maximum control effort u available from the actuator and the integral gain Ki, what might be a reasonable choice for the maximum value of eint? (Think about it; there is no single right answer, and in fact integrator antiwindup can be implemented in a number of different ways.)

For more information, see http://nu32.org. This video is a supplement to the book "Embedded Computing and Mechatronics with the PIC32 Microcontroller," Lynch, Marchuk, and Elwin. It is part of Northwestern University's ME 333Introduction to Mechatronics.
L-comp: Limiting the maximum value of the integral term eint is called "integrator antiwindup." (You are preventing the integrator from "winding up" to a large value.) In terms of the maximum control effort u available from the actuator and the integral gain Ki, what might be a reasonable choice for the maximum value of eint? (Think about it; there is no single right answer, and in fact integrator antiwindup can be implemented in a number of different ways.)

Calibration or tuning of radio propagation models (algorithms)

Our tiny company, dealing with radio network design, asks institutions interested in the development of applied physics and technology to finance studies of propagation models.
The idea is measuring the level of electromagnetic fields with a test car (which we have) around the test transmitters. Then, the results are used to develop the parameters of propagation model. The parameters depend on the type of terrain. That is why we plan to measure the many locations across Europe.
We believe that this will contribute greatly to the development of radio communication technology.
More info: j.jarzina@tele-com.poznan.pl
+48 602 42 66 97

FICO® Xpress Mosel #18: Tuning Xpress Solver and Algorithms

In this video, you will learn how to use Xpress Tuner to tune the internal algorithms of Xpress Optimizer. The Xpress Tuner feature is part of FICO Xpress Workbench®, one of four components of FICO® XpressOptimization that you learned about in the first tutorial in the series. The internal algorithms of Xpress Optimizer are user customizable through control parameters. With Xpress Tuner, you can find a favorable set of control parameters that allow the Xpress Optimizer to solve a MIP problem (or set of problems) faster than using the default settings. It is designed to be easy to use so you do not have to be an expert to use Xpress Tuner. This is the final video of the 18-part tutorial series on Xpress Mosel, FICO’s market-leading analytic orchestration, optimization modeling, and programming language. To learn more, please visit www.fico.com/optimization.

IBM QRadar UBA Tuning Machine Learning Algorithms

This video explains how to configure or tune the IBM QRadar UBAMachine Learning algorithms. If this video is helpful and saves you time to setup configure the machine learning algorithm, please give it a thumbs up and we can produce more of such videos to help you get the most value from your UBA App.

PID Tuning: The Ziegler Nichols Method Explained

In this short tutorial I will take you through the two Ziegler-Nichols tuning methods. This will let you tune the derivative, proportional and integral gains on your P, PI or PID controller. Thankfully these methods are extremely simple to understand and implement.

PID Control - A brief introduction

Check out my newer videos on PID control! http://bit.ly/2KGbPuy
I'm writing a book on the fundamentals of control theory!
Get the book-in-progress with any contribution for my work on Patreon - https://www.patreon.com/briandouglas
In this video, I introduce the topic of PID control. This is a short introduction design to prepare you for the next few lectures where I will go through several examples of PID control. This video explains why we need feedback control and how PID controller are simple and efficient ways to ensure you have a feed back system that meets your requirements.
I will be loading a new video each week and welcome suggestions for new topics. Please leave a comment or question below and I will do my best to address it. Thanks for watching!
Don't forget to subscribe! Follow me on Twitter @BrianBDouglas!

Read the complete tutorial about how to implement a machine learning algorithm for the Flappy Bird video game here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird
This video shows a creation of an artificial intelligence controller for the Flappy Bird game using neural networks and a genetic algorithm.
The program is written in HTML5 using Phaser framework (http://phaser.io/). There is also used Synaptic Neural Network library (https://synaptic.juancazala.com/) to implement entire artificial neural network instead of making a new one from the scratch.
Download source code here:
https://github.com/ssusnic/Machine-Learning-Flappy-Bird
-----------------------------------------------------------------
According to Arthur Samuel, machine learning is the science of getting computers to act without being explicitly programmed. It is a fine tuning process of learning that incrementally improves an initial random system.
The form of machine learning implemented in this program uses a genetic algorithm to train artificial neural networks.
The birds are learning how to flap optimally in order to fly safely through barriers as long as possible.
The main concept is based on these 3 steps:
1. creating the initial population of 10 birds randomly
2. learning as the game is being played
3. applying natural evolution to form the next improved population
-----------------------------------------------------------------
To play the game, each bird has its own neural network consisted of the next 3 layers:
1. an input layer with 2 neurons representing what a bird sees:
- horizontal distance to the closest gap
- height difference to the closest gap
2. a hidden layer with 6 neurons
3. an output layer with 1 neuron to perform flap if its value is greater than 0.5
-----------------------------------------------------------------
To measure the quality of birds and select the best ones, for each bird is calculated its fitness function in this way:
- reward a bird by its total traveled distance
- penalize a bird by its current distance to the closest gap
When the entire population is dead, the fittest four birds are selected to breed a new population by using genetic algorithm operators: selection, crossover and mutation.
-----------------------------------------------------------------
Visit us:
http://www.askforgametask.com
Like us:
https://www.facebook.com/askforgametask
Follow us:
https://twitter.com/ssusnic
Music:
"BedtimeTune" by Jay Man
http://www.ourmusicbox.com

Improving the basic PID control algorithm (Kevin Lynch)

For more information, see http://nu32.org. This video is a supplement to the book "Embedded Computing and Mechatronics with the PIC32 Microcontroller," Lynch, Marchuk, and Elwin. It is part of Northwestern University's ME 333Introduction to Mechatronics.
L-comp: Limiting the maximum value of the integral term eint is called "integrator antiwindup." (You are preventing the integrator from "winding up" to a large value.) In terms of the maximum control effort u available from the actuator and the integral gain Ki, what might be a reasonable choice for the maximum value of eint? (Think about it; there is no single right answer, and in fact integrator antiwindup can be implemented in a number of different ways.)